The DWD climate predictions website: Towards a seamless outlook based on subseasonal, seasonal and decadal predictions

The climate predictions website of the Deutscher Wetterdienst (DWD, https://www.dwd.de/climatepredictions) presents a consistent operational outlook for the coming weeks, months and years, focusing on the needs of German users. At global scale, subseasonal predictions from the European Centre of Medium-Range Weather Forecasts as well as seasonal and decadal predictions from the DWD are used. Statistical downscaling is applied to achieve high resolution over Germany. Lead-time dependent bias correction is performed on all time scales. Additionally, decadal predictions are recalibrated. The website offers ensemble mean and probabilistic predictions for temperature and precipitation combined with their skill (mean squared error skill score, ranked probability skill score). Two levels of complexity are offered: basic climate predictions display simple, regionally averaged information for Germany, German regions and cities as maps, time series and tables. The skill is presented as traffic light. Expert climate predictions show complex, gridded predictions for Germany (at high resolution), Europe and the world as maps and time series. The skill is displayed as the size of dots. Their color is related to the signal in the prediction. The website was developed in cooperation with users from different sectors via surveys, workshops and meetings to guarantee its understandability and usability. The users realize the potential of climate predictions, but some need advice in using probabilistic predictions and skill. Future activities will include the further development of predictions to improve skill (multi-model ensembles, teleconnections), the introduction of additional products (data provision, extremes) and the further clarification of the information (interactivity, video clips)

Adapting agricultural water use to climate change in a post-Soviet context: challenges and opportunities in southeast Kazakhstan

The convergence of climate change and post-Soviet socio-economic and institutional transformations has been underexplored so far, as have the consequences of such convergence on crop agriculture in Central Asia. This paper provides a place-based analysis of constraints and opportunities for adaptation to climate change, with a specific focus on water use, in two districts in southeast Kazakhstan. Data were collected by 2 multi-stakeholder participatory workshops, 21 semi-structured in-depth interviews, and secondary statistical data. The present-day agricultural system is characterised by enduring Soviet-era management structures, but without state inputs that previously sustained agricultural productivity. Low margins of profitability on many privatised farms mean that attempts to implement integrated water management have produced water users associations unable to maintain and upgrade a deteriorating irrigation infrastructure. Although actors engage in tactical adaptation measures, necessary structural adaptation of the irrigation system remains difficult without significant public or private investments. Market-based water management models have been translated ambiguously to this region, which fails to encourage efficient water use and hinders adaptation to water stress. In addition, a mutual interdependence of informal networks and formal institutions characterises both state governance and everyday life in Kazakhstan. Such interdependence simultaneously facilitates operational and tactical adaptation, but hinders structural adaptation, as informal networks exist as a parallel system that achieves substantive outcomes while perpetuating the inertia and incapacity of the state bureaucracy. This article has relevance for critical understanding of integrated water management in practice and adaptation to climate change in post-Soviet institutional settings more broadly

Northern Eurasia Future Initiative (NEFI): facing the challenges and pathways of global change in the 21st century

During the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies co-designed with regional decision makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include: warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land-use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia's role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large scale water withdrawals, land use and governance change) and potentially restrict or provide new opportunities for future human activities. Therefore, we propose that Integrated Assessment Models are needed as the final stage of global change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts

Dynamical downscaling of climate change in Central Asia

The high-resolution regional climate model (RCM) REMO has been implemented over the region of Central Asia, including western China. A model run forced by reanalysis data (1/2° resolution), and two runs forced by a GCM (one run with 1/2° and one run with 1/6° resolution) have been realized. The model has been evaluated regarding its ability to simulate the mean climate of the period 1971-2000. It has been found that the spatial pattern of mean temperature and precipitation is simulated well by REMO. The REMO simulations are often closer to observational data than reanalysis data are, and show considerably higher spatial detail. The GCM-forced simulations extend to the year 2100 under the A1B scenario. The climate change signal of temperature is largest in winter in the northern part of the study area and over mountainous terrain. A warming up to 7. °C is projected until the end of the 21st century. In summer, warming is strongest over the southern part of Central Asia. Changes in precipitation are spatially more heterogeneous. © 2013 Elsevier B.V

The DWD climate predictions website: Towards a seamless outlook based on subseasonal, seasonal and decadal predictions

The climate predictions website of the Deutscher Wetterdienst (DWD, https://www.dwd.de/climatepredictions) presents a consistent operational outlook for the coming weeks, months and years, focusing on the needs of German users. At global scale, subseasonal predictions from the European Centre of Medium-Range Weather Forecasts as well as seasonal and decadal predictions from the DWD are used. Statistical downscaling is applied to achieve high resolution over Germany. Lead-time dependent bias correction is performed on all time scales. Additionally, decadal predictions are recalibrated.The website offers ensemble mean and probabilistic predictions for temperature and precipitation combined with their skill (mean squared error skill score, ranked probability skill score). Two levels of complexity are offered: basic climate predictions display simple, regionally averaged information for Germany, German regions and cities as maps, time series and tables. The skill is presented as traffic light. Expert climate predictions show complex, gridded predictions for Germany (at high resolution), Europe and the world as maps and time series. The skill is displayed as the size of dots. Their color is related to the signal in the prediction.The website was developed in cooperation with users from different sectors via surveys, workshops and meetings to guarantee its understandability and usability. The users realize the potential of climate predictions, but some need advice in using probabilistic predictions and skill. Future activities will include the further development of predictions to improve skill (multi-model ensembles, teleconnections), the introduction of additional products (data provision, extremes) and the further clarification of the information (interactivity, video clips)